// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

#include <include/structure_table.h>

namespace PaddleOCR
{

    void StructureTableRecognizer::Run(
        std::vector<cv::Mat> img_list,
        std::vector<std::vector<std::string>> &structure_html_tags,
        std::vector<float> &structure_scores,
        std::vector<std::vector<std::vector<int>>> &structure_boxes,
        std::vector<double> &times)
    {
        std::chrono::duration<float> preprocess_diff =
            std::chrono::steady_clock::now() - std::chrono::steady_clock::now();
        std::chrono::duration<float> inference_diff =
            std::chrono::steady_clock::now() - std::chrono::steady_clock::now();
        std::chrono::duration<float> postprocess_diff =
            std::chrono::steady_clock::now() - std::chrono::steady_clock::now();

        int img_num = img_list.size();
        for (int beg_img_no = 0; beg_img_no < img_num;
             beg_img_no += this->table_batch_num_)
        {
            // preprocess
            auto preprocess_start = std::chrono::steady_clock::now();
            int end_img_no = std::min(img_num, beg_img_no + this->table_batch_num_);
            int batch_num = end_img_no - beg_img_no;
            std::vector<cv::Mat> norm_img_batch;
            std::vector<int> width_list;
            std::vector<int> height_list;
            for (int ino = beg_img_no; ino < end_img_no; ino++)
            {
                cv::Mat srcimg;
                img_list[ino].copyTo(srcimg);
                cv::Mat resize_img;
                cv::Mat pad_img;
                this->resize_op_.Run(srcimg, resize_img, this->table_max_len_);
                this->normalize_op_.Run(&resize_img, this->mean_, this->scale_,
                                        this->is_scale_);
                this->pad_op_.Run(resize_img, pad_img, this->table_max_len_);
                norm_img_batch.push_back(pad_img);
                width_list.push_back(srcimg.cols);
                height_list.push_back(srcimg.rows);
            }

            std::vector<float> input(
                batch_num * 3 * this->table_max_len_ * this->table_max_len_, 0.0f);
            this->permute_op_.Run(norm_img_batch, input.data());
            auto preprocess_end = std::chrono::steady_clock::now();
            preprocess_diff += preprocess_end - preprocess_start;
            // inference.
            auto input_names = this->predictor_->GetInputNames();
            auto input_t = this->predictor_->GetInputHandle(input_names[0]);
            input_t->Reshape(
                {batch_num, 3, this->table_max_len_, this->table_max_len_});
            auto inference_start = std::chrono::steady_clock::now();
            input_t->CopyFromCpu(input.data());
            this->predictor_->Run();
            auto output_names = this->predictor_->GetOutputNames();
            auto output_tensor0 = this->predictor_->GetOutputHandle(output_names[0]);
            auto output_tensor1 = this->predictor_->GetOutputHandle(output_names[1]);
            std::vector<int> predict_shape0 = output_tensor0->shape();
            std::vector<int> predict_shape1 = output_tensor1->shape();

            int out_num0 = std::accumulate(predict_shape0.begin(), predict_shape0.end(),
                                           1, std::multiplies<int>());
            int out_num1 = std::accumulate(predict_shape1.begin(), predict_shape1.end(),
                                           1, std::multiplies<int>());
            std::vector<float> loc_preds;
            std::vector<float> structure_probs;
            loc_preds.resize(out_num0);
            structure_probs.resize(out_num1);

            output_tensor0->CopyToCpu(loc_preds.data());
            output_tensor1->CopyToCpu(structure_probs.data());
            auto inference_end = std::chrono::steady_clock::now();
            inference_diff += inference_end - inference_start;
            // postprocess
            auto postprocess_start = std::chrono::steady_clock::now();
            std::vector<std::vector<std::string>> structure_html_tag_batch;
            std::vector<float> structure_score_batch;
            std::vector<std::vector<std::vector<int>>> structure_boxes_batch;
            this->post_processor_.Run(loc_preds, structure_probs, structure_score_batch,
                                      predict_shape0, predict_shape1,
                                      structure_html_tag_batch, structure_boxes_batch,
                                      width_list, height_list);
            for (int m = 0; m < predict_shape0[0]; m++)
            {

                structure_html_tag_batch[m].insert(structure_html_tag_batch[m].begin(),
                                                   "<table>");
                structure_html_tag_batch[m].insert(structure_html_tag_batch[m].begin(),
                                                   "<body>");
                structure_html_tag_batch[m].insert(structure_html_tag_batch[m].begin(),
                                                   "<html>");
                structure_html_tag_batch[m].push_back("</table>");
                structure_html_tag_batch[m].push_back("</body>");
                structure_html_tag_batch[m].push_back("</html>");
                structure_html_tags.push_back(structure_html_tag_batch[m]);
                structure_scores.push_back(structure_score_batch[m]);
                structure_boxes.push_back(structure_boxes_batch[m]);
            }
            auto postprocess_end = std::chrono::steady_clock::now();
            postprocess_diff += postprocess_end - postprocess_start;
            times.push_back(double(preprocess_diff.count() * 1000));
            times.push_back(double(inference_diff.count() * 1000));
            times.push_back(double(postprocess_diff.count() * 1000));
        }
    }

    void StructureTableRecognizer::LoadModel(const std::string &model_dir)
    {
        paddle_infer::Config config;
        config.SetModel(model_dir + "/inference.pdmodel",
                        model_dir + "/inference.pdiparams");

        if (this->use_gpu_)
        {
            config.EnableUseGpu(this->gpu_mem_, this->gpu_id_);
            if (this->use_tensorrt_)
            {
                auto precision = paddle_infer::Config::Precision::kFloat32;
                if (this->precision_ == "fp16")
                {
                    precision = paddle_infer::Config::Precision::kHalf;
                }
                if (this->precision_ == "int8")
                {
                    precision = paddle_infer::Config::Precision::kInt8;
                }
                config.EnableTensorRtEngine(1 << 20, 10, 3, precision, false, false);
                if (!Utility::PathExists("./trt_table_shape.txt"))
                {
                    config.CollectShapeRangeInfo("./trt_table_shape.txt");
                }
                else
                {
                    config.EnableTunedTensorRtDynamicShape("./trt_table_shape.txt", true);
                }
            }
        }
        else
        {
            config.DisableGpu();
            if (this->use_mkldnn_)
            {
                config.EnableMKLDNN();
            }
            else
            {
                config.DisableMKLDNN();
            }
            config.SetCpuMathLibraryNumThreads(this->cpu_math_library_num_threads_);
        }

        // false for zero copy tensor
        config.SwitchUseFeedFetchOps(false);
        // true for multiple input
        config.SwitchSpecifyInputNames(true);

        config.SwitchIrOptim(true);

        config.EnableMemoryOptim();
        config.DisableGlogInfo();

        this->predictor_ = paddle_infer::CreatePredictor(config);
    }
} // namespace PaddleOCR
